Exploratory Causal Analysis in Bivariate Time Series Data

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dc.contributor.advisorWeigel, Robert S.
dc.contributor.authorMcCracken, James M.
dc.creatorMcCracken, James M.
dc.date.accessioned2016-04-19T19:28:48Z
dc.date.available2016-04-19T19:28:48Z
dc.date.issued2015
dc.description.abstractMany scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics.
dc.format.extent172 pages
dc.identifier.urihttps://hdl.handle.net/1920/10169
dc.language.isoen
dc.rightsCopyright 2015 James M. McCracken
dc.subjectPhysics
dc.subjectCausality
dc.subjectGranger
dc.subjectLeaning
dc.subjectPairwise asymmetric inference
dc.subjectPenchant
dc.subjectTime series analysis
dc.titleExploratory Causal Analysis in Bivariate Time Series Data
dc.typeDissertation
thesis.degree.disciplinePhysics
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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